FlowDist: Multi-Staged Refinement-Based Dynamic Information Flow Analysis for Distributed Software Systems
FlowDist: Multi-Staged Refinement-Based Dynamic Information Flow Analysis for Distributed Software Systems
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发表时间：
2021
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通讯作者：
Xiaoqin Fu;Haipeng Cai
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作者：
Xiaoqin Fu;Haipeng Cai
动态信息分析（DIFA）支持各种安全性应用程序，例如恶意软件分析和漏洞发现。克服这些挑战的分布式软件在纯粹的应用级别上工作因此，自定义实现了较高的便携性。数据级分析通过廉价的预分析降低，以实现高可扩展性，同时保持对F的评估。在12个现实世界中，针对两个同行工具的DIST揭示了其效率和可扩展性的卓越有效性。 F低D IST用于设计合理和不同的主题住宿。
Dynamic information flow analysis (DIFA) supports various security applications such as malware analysis and vulnerability discovery. Yet traditional DIFA approaches have limited utility for distributed software due to applicability , portability , and scalability barriers. We present F LOW D IST , a DIFA for common distributed software that overcomes these challenges. F LOW D IST works at purely application level to avoid platform customizations hence achieve high portability . It infers implicit, interprocess dependencies from global partially ordered execution events to address applicability to distributed software. Most of all, it introduces a multi-staged refinement-based scheme for application-level DIFA, where an otherwise expensive data flow analysis is reduced by method-level results from a cheap pre-analysis, to achieve high scalability while remaining effective. Our evaluation of F LOW D IST on 12 real-world distributed systems against two peer tools revealed its superior effectiveness with practical efficiency and scalability. It has found 18 known and 24 new vulnerabilities, with 17 confirmed and 2 fixed. We also present and evaluate two alternative designs of F LOW D IST for both design justification and diverse subject accommodations.